Search engines are essential tools for retrieving and exploring extraordinary large collections of information sources available on the World Wide Web. As the World Wide Web has grown, the ability of users to search this collection of information and identify content relevant to a particular subject has become increasingly important. To a large extent, users determine the quality of a search engine by the ranking function that a given search engine uses to produce search results responsive to a given search query. Thus, to be useful, a search engine should determine those content items in a given result set that are most relevant to the user on the basis of the query that the user submits and rank such content items accordingly.
A determination as to those content items that are relevant to the query is influenced by a number of factors, many of which are highly subjective. Due to the highly subjective nature of such factors, it is generally difficult to capture in an algorithmic set of rules factors that define a function for ranking content items. Furthermore, these subjective factors may change over time, as for example when current events are associated with a particular query term. Thus, users who receive search result sets that contain results the user does not perceive to be highly relevant become frustrated and potentially abandon the use of a search engine. Therefore, designing an effective and efficient function that retrieves and efficiently ranks content items is of the upmost importance to the field of information retrieval.
Research and experiments in information retrieval in the past have produced many fundamental methodologies and algorithms in an attempt to solve this problem, including vector space models, probabilistic models and language modeling-based methodologies. Recently, machine learning methods have become an important tool in the retrieval and ranking of content items responsive to a search query. Such machine learning methods, however, have certain limitations. In particular, existing machine learning methods for learning ranking functions fail to take account of all preference data within a query to allow for faster convergence. Therefore, there exists a need for an improved machine learning method for learning ranking functions that is directed to a more global approach to solving issues of relevance in the retrieval of content items in response to a query.